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Semantic analysis of field sports video using a petri-net of audio-visual concepts
Date
2009
Abstract
The most common approach to automatic summarisation and highlight detection in sports video is to train an automatic classi er to detect semantic highlights based on occurrences of low-level features such as action replays, excited commentators or changes in a scoreboard. We propose an alternative approach based on the detection of perception concepts (PCs) and the construction of Petri-Nets which can be used for both semantic description and event detection within sports videos. Low-level algorithms for the detection of perception concepts using visual, aural and motion characteristics are proposed, and a series of Petri-Nets composed of perception concepts is formally de ned to describe video content. We call this a Perception Concept Network-Petri Net (PCN-PN) model. Using PCN-PNs, personalized high-level semantic descriptions of video highlights can be facilitated and queries on high-level semantics can be achieved. A particular strength of this framework is that we can easily build semantic detectors based on PCN-PNs to search within sports videos and locate interesting events. Experimental results based on recorded sports video data across three types of sports games (soccer, basketball and rugby), and each from multiple broadcasters, are used to illustrate the potential of this framework.
Supervisor
Description
peer-reviewed
Publisher
Oxford University Press
Citation
The Computer Journal;52(7), pp. 808-823
Files
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Bai_2008_semantics.pdf
Adobe PDF, 697.69 KB
Keywords
Funding code
Funding Information
National High Technology Development 863 Program of China, National Natural Science Foundation of China, Science Foundation Ireland (SFI)
Sustainable Development Goals
External Link
Type
Article
Rights
https://creativecommons.org/licenses/by-nc-sa/1.0/